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Cross-Validation in Regression and Covariance Structure Analysis

Author

Listed:
  • ASTREA CAMSTRA

    (University of Groningen)

  • ANNE BOOMSMA

    (University of Groningen)

Abstract

This article gives an overview of cross-validation techniques in regression and covariance structure analysis. The method of cross-validation offers a means for checking the accuracy or reliability of results that were obtained by an exploratory analysis of the data. Cross-validation provides the possibility to select, from a set of alternative models, the model with the greatest predictive validity, that is, the model that cross-validates best. The disadvantage of cross-validation is that the data need to be split in two or more parts. This can be a serious problem when sample size is small. Various authors have therefore tried to find single sample criteria that provide the same kind of information as the cross-validation criteria but that do not require the use of a validation sample. Several of these criteria will be discussed, along with some results from studies comparing cross-validation and single sample criteria in covariance structure analysis.

Suggested Citation

  • Astrea Camstra & Anne Boomsma, 1992. "Cross-Validation in Regression and Covariance Structure Analysis," Sociological Methods & Research, , vol. 21(1), pages 89-115, August.
    • Handle: RePEc:sae:somere:v:21:y:1992:i:1:p:89-115
    DOI: 10.1177/0049124192021001004
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    References listed on IDEAS

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    1. Claes Fornell & Roland Rust, 1989. "Incorporating prior theory in covariance structure analysis: A bayesian approach," Psychometrika, Springer;The Psychometric Society, vol. 54(2), pages 249-259, June.
    2. David Kaplan, 1991. "On the modification and predictive validity of covariance structure models," Quality & Quantity: International Journal of Methodology, Springer, vol. 25(3), pages 307-314, August.
    3. Roland T. Rust & David C. Schmittlein, 1985. "A Bayesian Cross-Validated Likelihood Method for Comparing Alternative Specifications of Quantitative Models," Marketing Science, INFORMS, vol. 4(1), pages 20-40.
    4. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
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    1. Hallak, Rob & Brown, Graham & Lindsay, Noel J., 2012. "The Place Identity – Performance relationship among tourism entrepreneurs: A structural equation modelling analysis," Tourism Management, Elsevier, vol. 33(1), pages 143-154.

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